






'''simple docstring'''







import json
from typing import List, Optional, Tuple

from tokenizers import pre_tokenizers, processors

from ...tokenization_utils_base import AddedToken, BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_bart import BartTokenizer


__lowerCAmelCase        =							logging.get_logger(__name__)


__lowerCAmelCase        =							{"vocab_file": "vocab.json", "merges_file": "merges.txt", "tokenizer_file": "tokenizer.json"}

# See all BART models at https://huggingface.co/models?filter=bart
__lowerCAmelCase        =							{
    "vocab_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/vocab.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/vocab.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/vocab.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/vocab.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/vocab.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/vocab.json",
    },
    "merges_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/merges.txt",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/merges.txt",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/merges.txt",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/merges.txt",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/merges.txt",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/merges.txt",
    },
    "tokenizer_file": {
        "facebook/bart-base": "https://huggingface.co/facebook/bart-base/resolve/main/tokenizer.json",
        "facebook/bart-large": "https://huggingface.co/facebook/bart-large/resolve/main/tokenizer.json",
        "facebook/bart-large-mnli": "https://huggingface.co/facebook/bart-large-mnli/resolve/main/tokenizer.json",
        "facebook/bart-large-cnn": "https://huggingface.co/facebook/bart-large-cnn/resolve/main/tokenizer.json",
        "facebook/bart-large-xsum": "https://huggingface.co/facebook/bart-large-xsum/resolve/main/tokenizer.json",
        "yjernite/bart_eli5": "https://huggingface.co/yjernite/bart_eli5/resolve/main/tokenizer.json",
    },
}

__lowerCAmelCase        =							{
    "facebook/bart-base": 10_24,
    "facebook/bart-large": 10_24,
    "facebook/bart-large-mnli": 10_24,
    "facebook/bart-large-cnn": 10_24,
    "facebook/bart-large-xsum": 10_24,
    "yjernite/bart_eli5": 10_24,
}




class   __SCREAMING_SNAKE_CASE       (__A       ):


  """simple docstring"""


  _a  : Any      			=     VOCAB_FILES_NAMES
  _a  : Tuple      			=     PRETRAINED_VOCAB_FILES_MAP
  _a  : Optional[Any]      			=     PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
  _a  : int      			=     ['''input_ids''', '''attention_mask''']
  _a  : Any      			=     BartTokenizer







  def __init__(			self   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__=None   ,	UpperCamelCase__="replace"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="</s>"   ,	UpperCamelCase__="<s>"   ,	UpperCamelCase__="<unk>"   ,	UpperCamelCase__="<pad>"   ,	UpperCamelCase__="<mask>"   ,	UpperCamelCase__=False   ,	UpperCamelCase__=True   ,	**UpperCamelCase__   ,	):






         """simple docstring"""


         super().__init__(
             UpperCamelCase__   ,	UpperCamelCase__   ,	tokenizer_file=UpperCamelCase__   ,	errors=UpperCamelCase__   ,	bos_token=UpperCamelCase__   ,	eos_token=UpperCamelCase__   ,	sep_token=UpperCamelCase__   ,	cls_token=UpperCamelCase__   ,	unk_token=UpperCamelCase__   ,	pad_token=UpperCamelCase__   ,	mask_token=UpperCamelCase__   ,	add_prefix_space=UpperCamelCase__   ,	trim_offsets=UpperCamelCase__   ,	**UpperCamelCase__   ,	)

         a_						     =			json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()	)
         if pre_tok_state.get('add_prefix_space'   ,	UpperCamelCase__	) != add_prefix_space:
                a_						     =			getattr(UpperCamelCase__   ,	pre_tok_state.pop('type'	)	)
                a_						     =			add_prefix_space
                a_						     =			pre_tok_class(**UpperCamelCase__	)

         a_						     =			add_prefix_space

         # the pre_tokenizer is already updated in the GPT2TokenizerFast `__init__`
         a_						     =			'post_processor'
         a_						     =			getattr(self.backend_tokenizer   ,	UpperCamelCase__   ,	UpperCamelCase__	)
         if tokenizer_component_instance:
                a_						     =			json.loads(tokenizer_component_instance.__getstate__()	)

                # The lists 'sep' and 'cls' must be cased in tuples for the object `post_processor_class`
                if "sep" in state:
                       a_						     =			tuple(state['sep']	)
                if "cls" in state:
                       a_						     =			tuple(state['cls']	)

                a_						     =			False

                if state.get('add_prefix_space'   ,	UpperCamelCase__	) != add_prefix_space:
                       a_						     =			add_prefix_space
                       a_						     =			True

                if state.get('trim_offsets'   ,	UpperCamelCase__	) != trim_offsets:
                       a_						     =			trim_offsets
                       a_						     =			True

                if changes_to_apply:
                       a_						     =			getattr(UpperCamelCase__   ,	state.pop('type'	)	)
                       a_						     =			component_class(**UpperCamelCase__	)
                       setattr(self.backend_tokenizer   ,	UpperCamelCase__   ,	UpperCamelCase__	)







  @property
  def    _a       (			self	):






         """simple docstring"""


         if self._mask_token is None:
                if self.verbose:
                       logger.error('Using mask_token, but it is not set yet.'	)
                return None
         return str(self._mask_token	)







  @mask_token.setter
  def    _a       (			self   ,	UpperCamelCase__	):






         """simple docstring"""


         a_						     =			AddedToken(UpperCamelCase__   ,	lstrip=UpperCamelCase__   ,	rstrip=UpperCamelCase__	) if isinstance(UpperCamelCase__   ,	UpperCamelCase__	) else value
         a_						     =			value







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			kwargs.get('is_split_into_words'   ,	UpperCamelCase__	)

         if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                    'to use it with pretokenized inputs.'	)

         return super()._batch_encode_plus(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	*UpperCamelCase__   ,	**UpperCamelCase__	):






         """simple docstring"""


         a_						     =			kwargs.get('is_split_into_words'   ,	UpperCamelCase__	)

         if is_split_into_words and not self.add_prefix_space:
                raise ValueError(
                    f'You need to instantiate {self.__class__.__name__} with add_prefix_space=True '
                    'to use it with pretokenized inputs.'	)

         return super()._encode_plus(*UpperCamelCase__   ,	**UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			self._tokenizer.model.save(UpperCamelCase__   ,	name=UpperCamelCase__	)
         return tuple(UpperCamelCase__	)







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__=None	):






         """simple docstring"""


         a_						     =			[self.bos_token_id] + token_ids_a + [self.eos_token_id]
         if token_ids_a is None:
                return output

         return output + [self.eos_token_id] + token_ids_a + [self.eos_token_id]







  def    _a       (			self   ,	UpperCamelCase__   ,	UpperCamelCase__ = None	):






         """simple docstring"""


         a_						     =			[self.sep_token_id]
         a_						     =			[self.cls_token_id]

         if token_ids_a is None:
                return len(cls + token_ids_a + sep	) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep	) * [0]












'''simple docstring'''







from typing import TYPE_CHECKING

from ...utils import OptionalDependencyNotAvailable, _LazyModule, is_torch_available, is_vision_available


__lowerCAmelCase        =							{"configuration_glpn": ["GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP", "GLPNConfig"]}

try:
  if not is_vision_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							["GLPNFeatureExtractor"]
  __lowerCAmelCase        =							["GLPNImageProcessor"]

try:
  if not is_torch_available():
    raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
  pass
else:
  __lowerCAmelCase        =							[
      "GLPN_PRETRAINED_MODEL_ARCHIVE_LIST",
      "GLPNForDepthEstimation",
      "GLPNLayer",
      "GLPNModel",
      "GLPNPreTrainedModel",
  ]


if TYPE_CHECKING:
  from .configuration_glpn import GLPN_PRETRAINED_CONFIG_ARCHIVE_MAP, GLPNConfig

  try:
    if not is_vision_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .feature_extraction_glpn import GLPNFeatureExtractor
    from .image_processing_glpn import GLPNImageProcessor

  try:
    if not is_torch_available():
      raise OptionalDependencyNotAvailable()
  except OptionalDependencyNotAvailable:
    pass
  else:
    from .modeling_glpn import (
        GLPN_PRETRAINED_MODEL_ARCHIVE_LIST,
        GLPNForDepthEstimation,
        GLPNLayer,
        GLPNModel,
        GLPNPreTrainedModel,
    )


else:
  import sys

  __lowerCAmelCase        =							_LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




